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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38264bit8e574678bcfb4583b0ce2045be636689",
   "display_name": "Python 3.8.2 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}